lenarddome · Oct 16, 2026 · Oct 16, 2026
diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -5,28 +5,7 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
-## [0.26.0.dev3] - 2026-10-14
-
-### Added
-
-- Added `benchmarks/hierarchical_profile.py`, which shows where the time of a `VariationalBayes` fit goes and counts the objective evaluations per participant and iteration; against cbm (RLRW, 100 participants, 120 trials), cpm makes about as many evaluations but each one costs more, at 17.3 against 5.0 ms per participant and iteration
-- Added a `hessian` argument to `cpm.optimisation.FminBound`, `Fmin`, `Minimize` and `Bads`; `hessian="finite_differences"` computes the Hessian at the optimum with bounded finite differences (`cpm.core.optimisers.finite_difference_hessian`), cutting Hessian evaluations from 22 to 7 per participant and iteration in the speed comparison. The default, `"numdifftools"`, keeps the previous Hessian
-- Added a `start` argument to `cpm.hierarchical.EmpiricalBayes` and `cpm.hierarchical.VariationalBayes`; `start="prior_mean"` starts the first optimisation of every participant from the current prior's location, clipped into the bounds, as cbm does, so fits stop sooner. The default, `"random"`, keeps the previous starts
-- Added a `positive_definite` column to the `fit` of `cpm.hierarchical.EmpiricalBayes` and `cpm.hierarchical.VariationalBayes`, recording whether each participant's Hessian was positive definite, and a `not_positive_definite` column to their `hyperparameters` with the count; an estimate on a bound can have a non-positive-definite Hessian and is kept, as the bounds require
-- Added the results of the speed comparison with cbm (100 participants, 120 trials) in `benchmarks/results/hierarchical-comparison-*.csv`: combining `hessian="finite_differences"`, `start="prior_mean"` and `number_of_starts=1` brings `EmpiricalBayes`/`VariationalBayes` from 20.8/20.1 to 6.8/7.0 ms per participant and iteration, against cbm's 4.4 ms, with RMSE against 0.26.0.dev1 staying within bootstrap 95% intervals throughout
-
-### Changed
-
-- `cpm.optimisation.FminBound` with `approx_grad=True` now computes the finite-difference gradient itself, together with the objective, exactly as SciPy does, with identical results; with `LogLikelihood.bernoulli`, this makes the hierarchical fits of the speed comparison 19% faster
-- The defaults of the new arguments, `hessian="numdifftools"` and `start="random"`, give the same results as before for the same seed; the faster options have to be chosen
-
-### Fixed
-
-- Fixed fits with `parallel=True` and `prior=True` going back to the priors the model was created with after the first evaluation in every worker, so that priors updated with `update_prior`, such as the population priors of `cpm.hierarchical.EmpiricalBayes` and `cpm.hierarchical.VariationalBayes`, were ignored; parallel fits now equal serial ones, and parallel results with updated priors change. `cpm.generators` no longer deletes its submodules, which made dill copy its classes into the workers instead of referencing them
-- Fixed `Fmin`, `FminBound`, `Minimize` and `Bads` comparing every later start with the first instead of the best so far, which with three or more starts could replace a participant's best fit with a worse one; results with three or more starts can change
-- Fixed the `cpm.hierarchical.VariationalBayes` docstring giving 1000 as the default of `iteration`, which is 50
-
-## [0.26.0.dev2] - 2026-10-06
+## [0.26.0] - 2026-10-16
 
 ### Added
 
@@ -51,6 +30,11 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Added test units for `SessionWrapper`, `cpm.models.kernels`, the fast priors, and the built-in applications with and without numba
 - Added a benchmark suite (`benchmarks/run.py` for one evaluation, `benchmarks/hierarchical.py` for the hierarchical tutorials) and `scripts/local_tests.py`, which runs the tests with and without numba
 - Added a GitHub Actions workflow that runs the tests with and without numba, on Python 3.11 to 3.14
+- Added `benchmarks/hierarchical_profile.py`, which shows where the time of a `VariationalBayes` fit goes and counts the objective evaluations per participant and iteration; against cbm (RLRW, 100 participants, 120 trials), cpm makes about as many evaluations but each one costs more, at 17.3 against 5.0 ms per participant and iteration
+- Added a `hessian` argument to `cpm.optimisation.FminBound`, `Fmin`, `Minimize` and `Bads`; `hessian="finite_differences"` computes the Hessian at the optimum with bounded finite differences (`cpm.core.optimisers.finite_difference_hessian`), cutting Hessian evaluations from 22 to 7 per participant and iteration in the speed comparison. The default, `"numdifftools"`, keeps the previous Hessian
+- Added a `start` argument to `cpm.hierarchical.EmpiricalBayes` and `cpm.hierarchical.VariationalBayes`; `start="prior_mean"` starts the first optimisation of every participant from the current prior's location, clipped into the bounds, as cbm does, so fits stop sooner. The default, `"random"`, keeps the previous starts
+- Added a `positive_definite` column to the `fit` of `cpm.hierarchical.EmpiricalBayes` and `cpm.hierarchical.VariationalBayes`, recording whether each participant's Hessian was positive definite, and a `not_positive_definite` column to their `hyperparameters` with the count; an estimate on a bound can have a non-positive-definite Hessian and is kept, as the bounds require
+- Added the results of the speed comparison with cbm (100 participants, 120 trials) in `benchmarks/results/hierarchical-comparison-*.csv`: combining `hessian="finite_differences"`, `start="prior_mean"` and `number_of_starts=1` brings `EmpiricalBayes`/`VariationalBayes` from 20.8/20.1 to 6.8/7.0 ms per participant and iteration, against cbm's 4.4 ms, with RMSE against 0.26.0.dev1 staying within bootstrap 95% intervals throughout
 
 ### Changed
 
@@ -70,6 +54,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - `cpm.models.activation.ProspectUtility.weights` and `.utilities` are float arrays instead of object arrays, and the `simulation` records of `HybridMBMF` hold NumPy scalars instead of Python numbers
 - `PTSM2025` always uses its power utility: replacing `parameters.utility_curvature` on a model no longer changes it
 - Required Python 3.11 or later (previously `>3.11.0`, which excluded 3.11.0), removed the PyPy classifier, and tagged the wheel for Python 3 only
+- `cpm.optimisation.FminBound` with `approx_grad=True` now computes the finite-difference gradient itself, together with the objective, exactly as SciPy does, with identical results; with `LogLikelihood.bernoulli`, this makes the hierarchical fits of the speed comparison 19% faster
+- The defaults of the new arguments, `hessian="numdifftools"` and `start="random"`, give the same results as before for the same seed; the faster options have to be chosen
 
 ### Fixed
 
@@ -101,6 +87,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Fixed a clean install raising `ModuleNotFoundError` in `diagnostics()`, by adding `matplotlib` to the dependencies
 - Fixed `cpm.utils.data` and `cpm.utils.metad` not being reachable after `import cpm`
 - Fixed `cpm.core.diagnostics.gelman_rubin` and `cpm.core.diagnostics.psrf` failing on every call, and `gelman_rubin` computing the statistic incorrectly
+- Fixed fits with `parallel=True` and `prior=True` going back to the priors the model was created with after the first evaluation in every worker, so that priors updated with `update_prior`, such as the population priors of `cpm.hierarchical.EmpiricalBayes` and `cpm.hierarchical.VariationalBayes`, were ignored; parallel fits now equal serial ones, and parallel results with updated priors change. `cpm.generators` no longer deletes its submodules, which made dill copy its classes into the workers instead of referencing them
+- Fixed `Fmin`, `FminBound`, `Minimize` and `Bads` comparing every later start with the first instead of the best so far, which with three or more starts could replace a participant's best fit with a worse one; results with three or more starts can change
+- Fixed the `cpm.hierarchical.VariationalBayes` docstring giving 1000 as the default of `iteration`, which is 50
 
 ## [0.25.6] - 2026-04-15
 

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
@@ -209,11 +209,18 @@ Best ways to get help:
 
 ## Build and release notes
 
-If you are preparing a release artifact:
+To prepare a release:
 
-```bash
-python setup.py sdist bdist_wheel
-```
+1. Set the version in `cpm/__version__.py` and date its section in `CHANGELOG.md`.
+2. Build and check the source distribution and wheel:
+
+   ```bash
+   pip install build twine
+   python -m build
+   twine check dist/*
+   ```
+
+3. After merging into `main`, tag the release with the bare version (for example `0.26.0`, not `v0.26.0`); the documentation links to the source at this tag.
 
 Publishing to PyPI is maintainer-only and typically done with:
 

diff --git a/LICENSES-BUNDLE b/LICENSES-BUNDLE
@@ -4,16 +4,16 @@ The cpm repository and source distributions bundle a number of libraries that
 are compatibly licensed.  We list these here.
 
 Name: metadpy
-Files: cpm/application/signal_detection.py
+Files: cpm/applications/signal_detection.py
 License: GPLv3
-    For details, see header comments in cpm/application/signal_detection.py.
+    For details, see header comments in cpm/applications/signal_detection.py.
 
 Name: meta-d MATLAB scripts
 Files: cpm/applications/signal_detection.py
 License: None
-    For details, see header comments in cpm/application/signal_detection.py.
+    For details, see header comments in cpm/applications/signal_detection.py.
     The MATLAB scripts are not included in the cpm repository, but are
-    referenced in the cpm/application/signal_detection.py file.
+    referenced in the cpm/applications/signal_detection.py file.
     The MATLAB scripts are available at <https://www.columbia.edu/~bsm2105/type2sdt/>
     and were written by Brian Maniscalco. There is no accompanying license
     information, but they are still referenced here for completeness.
diff --git a/README.md b/README.md
@@ -69,7 +69,7 @@ python -m sphinx -b html docs docs/_build/html
 ```
 
 Then open `docs/_build/html/index.html` in a browser.
-See [CONTRIBUTING.md](CONTRIBUTING.md) for how to write documentation and tutorials, and for a live preview while you write.
+See [CONTRIBUTING.md](https://github.com/DevComPsy/cpm/blob/main/CONTRIBUTING.md) for how to write documentation and tutorials, and for a live preview while you write.
 
 The documentation is published to GitHub Pages by the `docs` GitHub Actions workflow whenever `main` changes.
 

diff --git a/cpm/__version__.py b/cpm/__version__.py
@@ -1 +1 @@
-__version__ = "0.26.0.dev3"
+__version__ = "0.26.0"
diff --git a/docs/news/2026-09-faster-fitting.md b/docs/news/2026-09-faster-fitting.md
@@ -7,7 +7,7 @@ kernelspec:
 
 # Faster fitting in cpm 0.26
 
-*28 September 2026 · cpm 0.26.0, in development*
+*28 September 2026 · cpm 0.26.0*
 
 The built-in models of cpm now compute all trials of a participant in one call instead of one call per trial, and, when [numba](https://numba.pydata.org/) is installed, that loop over trials is compiled.
 Fitting them is 90 to 220 times faster than in cpm 0.25, and more than 1,000 times faster for {py:class}`~cpm.applications.decision_making.PTSM2025`, with the same results.

diff --git a/pyproject.toml b/pyproject.toml
@@ -1,6 +1,6 @@
 [build-system]
 requires = [
-  "setuptools>=61.0",
+  "setuptools>=77.0",
 ]
 build-backend = "setuptools.build_meta"
 
@@ -20,15 +20,22 @@ authors = [
 maintainers = [
     { name = "Lenard Dome", email = "lenarddome@gmail.com" }
 ]
-license = { text = "AGPLv3" }
+license = "AGPL-3.0-only"
+license-files = ["LICENSE", "LICENSES-BUNDLE"]
 requires-python = ">=3.11"
 keywords = ["cpm-toolbox", "cpm", "computational-psychiatry", "computational-psychology", "psychometrics", "psychology"]
 classifiers = [
     "Development Status :: 4 - Beta",
     "Intended Audience :: Developers",
-    "License :: OSI Approved :: GNU Affero General Public License v3",
+    "Intended Audience :: Science/Research",
     "Natural Language :: English",
-    "Programming Language :: Python :: 3"
+    "Operating System :: OS Independent",
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
+    "Programming Language :: Python :: 3.13",
+    "Programming Language :: Python :: 3.14",
+    "Topic :: Scientific/Engineering"
 ]
 dependencies = [
     "numpy>=2.0.0",
@@ -78,6 +85,9 @@ notebooks = [
 [project.urls]
 Homepage = "https://cpm-toolbox.net"
 Documentation = "https://devcompsy.github.io/cpm/"
+Repository = "https://github.com/DevComPsy/cpm"
+Issues = "https://github.com/DevComPsy/cpm/issues"
+Changelog = "https://github.com/DevComPsy/cpm/blob/main/CHANGELOG.md"
 
 [tool.setuptools]
 include-package-data = true
Original file line number	Diff line number	Diff line change
		@@ -1 +1 @@
		__version__ = "0.26.0.dev3"
		__version__ = "0.26.0"